8 citations · 18 across the 25 of their papers we have counts for
17 papers · 1 filter
dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
Giuseppe Franco, Ian Colbert, Pablo Monteagudo-Lago +2
Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all lay…
Signed Symmetric Quantization for Few-Bit Integers
Ian Colbert, Eashan Dash, Pablo Monteagudo-Lago +5
The signed integer alphabet contains one more negative representable value than positive. Yet, by convention, the standard symmetric integer quantizer fixes its scale to be strictl…
Optimal Post-Training Quantization Scales and Where to Find Them
Juan Amboage, Pablo Monteagudo-Lago, Ian Colbert +2
Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. While much work has focused on how weights are rounded onto the qua…
Pushing the Limits of Block Rotations in Post-Training Quantization
Sai Sanjeet, Ian Colbert, Pablo Monteagudo-Lago +3
Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of online full-vector rotations,…
Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Haoyu Zhang, Shihao Zhang, Ian Colbert +1
Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including large language models (LLMs). Among P…
Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach
Daniel Campa, Mehdi Saeedi, Ian Colbert +1
Navigation path traces play a crucial role in video game design, serving as a vital resource for both enhancing player engagement and fine-tuning non-playable character behavior. G…
Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
Shihao Zhang, Haoyu Zhang, Ian Colbert +1
We introduce Qronos -- a new state-of-the-art post-training quantization algorithm that sequentially rounds and updates neural network weights. Qronos not only explicitly corrects…
Improving Quantization with Post-Training Model Expansion
Giuseppe Franco, Pablo Monteagudo-Lago, Ian Colbert +2
The size of a model has been a strong predictor of its quality, as well as its cost. As such, the trade-off between model cost and quality has been well-studied. Post-training opti…
Accumulator-Aware Post-Training Quantization for Large Language Models
Ian Colbert, Giuseppe Franco, Fabian Grob +2
When quantizing weights and activations to increasingly narrower representations, the cost of additions begins to dominate that of multiplications in multiply-accumulate (MAC) unit…
A2Q+: Improving Accumulator-Aware Weight Quantization
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig +1
Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the p…
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig
We present accumulator-aware quantization (A2Q), a novel weight quantization method designed to train quantized neural networks (QNNs) to avoid overflow when using low-precision ac…
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig
We introduce a quantization-aware training algorithm that guarantees avoiding numerical overflow when reducing the precision of accumulators during inference. We leverage weight no…
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
Alexander Cann, Ian Colbert, Ihab Amer
The widespread adoption of deep neural networks in computer vision applications has brought forth a significant interest in adversarial robustness. Existing research has shown that…
Generating GPU Compiler Heuristics using Reinforcement Learning
Ian Colbert, Jake Daly, Norm Rubin
GPU compilers are complex software programs with many optimizations specific to target hardware. These optimizations are often controlled by heuristics hand-designed by compiler ex…
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado +1
Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the…
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado +1
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data,…
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado +2
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, im…